Self-Correcting Distance Geometry for NMR Analysis and Protein Design

这 项目将提高稳定性，可靠性 Werner Braun教授及其同事的新计算工具--用于NMR结构测定和蛋白质设计的自校正距离几何（SECODG）方法--的效率。蛋白质，RNA或DNA的三维结构的计算SECODG方法从距离和二面角的限制。 与以前的距离几何方法，这是专为一致的数据集，SECODG方法可以生成精确的结构，甚至从包含错误的约束集。SECODG方法大大减少了从NMR数据生成蛋白质结构的时间。Braun博士的研究小组将进一步改进这种方法，并探索新的错误检测统计方法。 如果二级结构部分或完全已知，SECODG方法也可用于从氨基酸序列预测蛋白质的三级折叠。 它在初步研究中成功地测试了其从氨基酸序列预测小螺旋蛋白质折叠的能力，现在它将被应用于更广泛的蛋白质折叠。 SECODG方法将是一个强大的计算工具，以加快 大分子结构测定 NMR 数据 实验确定的蛋白质三维结构是设计具有改进或新功能的新药或蛋白质的基础。结合能量最小化和蒙特卡罗模拟，它将有助于设计具有给定结构和新功能特性的蛋白质。

This project will enhance the stability, reliability and efficiency of Prof. Werner Braun and coworkers' new computational tool, the self-correcting distance geometry (SECODG) method for NMR structure determination and protein design. Three-dimensional structures of proteins, RNA or DNA are calculated by the SECODG method from distance and dihedral angle constraints. Unlike previous distance geometric methods, which were designed for consistent data sets, the SECODG method can generate accurate structures even from sets of constraints that contain errors. The SECODG method dramatically reduces the time to generate protein structures from NMR data. Dr. Braun's group will further improve this method and explore new statistical methods for error detection. The SECODG method can also be used to predict the tertiary folds of proteins from the amino acid sequence, if the secondary structure is partially or completely known. It was successfully tested in a preliminary study for its ability to predict the fold of small helical proteins from their amino acid sequences, and it will now be applied to a broader range of protein folds. The SECODG method will be a powerful computational tool to speed up macromolecular structure determination from NMR data. Experimentally determined three-dimensional protein structures are the basis to design new drugs or proteins with improved or new functions. In combination with energy minimization and Monte Carlo simulations it will help in designing proteins with given structural and new functional properties.